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loss minimization</title> <meta name="description" content="Search results for: regularized loss minimization"> <meta name="keywords" content="regularized loss minimization"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="regularized loss minimization"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 3755</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: regularized loss minimization</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3755</span> Using of Particle Swarm Optimization for Loss Minimization of Vector-Controlled Induction Motors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=V.%20Rashtchi">V. Rashtchi</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Bizhani"> H. Bizhani</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20R.%20Tatari"> F. R. Tatari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a new online loss minimization for an induction motor drive. Among the many loss minimization algorithms (LMAs) for an induction motor, a particle swarm optimization (PSO) has the advantages of fast response and high accuracy. However, the performance of the PSO and other optimization algorithms depend on the accuracy of the modeling of the motor drive and losses. In the development of the loss model, there is always a trade off between accuracy and complexity. This paper presents a new online optimization to determine an optimum flux level for the efficiency optimization of the vector-controlled induction motor drive. An induction motor (IM) model in d-q coordinates is referenced to the rotor magnetizing current. This transformation results in no leakage inductance on the rotor side, thus the decomposition into d-q components in the steady-state motor model can be utilized in deriving the motor loss model. The suggested algorithm is simple for implementation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=induction%20machine" title="induction machine">induction machine</a>, <a href="https://publications.waset.org/abstracts/search?q=loss%20minimization" title=" loss minimization"> loss minimization</a>, <a href="https://publications.waset.org/abstracts/search?q=magnetizing%20current" title=" magnetizing current"> magnetizing current</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a> </p> <a href="https://publications.waset.org/abstracts/15437/using-of-particle-swarm-optimization-for-loss-minimization-of-vector-controlled-induction-motors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15437.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">633</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3754</span> A Family of Distributions on Learnable Problems without Uniform Convergence</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=C%C3%A9sar%20Garza">César Garza</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In supervised binary classification and regression problems, it is well-known that learnability is equivalent to a uniform convergence of the hypothesis class, and if a problem is learnable, it is learnable by empirical risk minimization. For the general learning setting of unsupervised learning tasks, there are non-trivial learning problems where uniform convergence does not hold. We present here the task of learning centers of mass with an extra feature that “activates” some of the coordinates over the unit ball in a Hilbert space. We show that the learning problem is learnable under a stable RLM rule. We introduce a family of distributions over the domain space with some mild restrictions for which the sample complexity of uniform convergence for these problems must grow logarithmically with the dimension of the Hilbert space. If we take this dimension to infinity, we obtain a learnable problem for which the uniform convergence property fails for a vast family of distributions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=statistical%20learning%20theory" title="statistical learning theory">statistical learning theory</a>, <a href="https://publications.waset.org/abstracts/search?q=learnability" title=" learnability"> learnability</a>, <a href="https://publications.waset.org/abstracts/search?q=uniform%20convergence" title=" uniform convergence"> uniform convergence</a>, <a href="https://publications.waset.org/abstracts/search?q=stability" title=" stability"> stability</a>, <a href="https://publications.waset.org/abstracts/search?q=regularized%20loss%20minimization" title=" regularized loss minimization"> regularized loss minimization</a> </p> <a href="https://publications.waset.org/abstracts/151038/a-family-of-distributions-on-learnable-problems-without-uniform-convergence" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151038.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">130</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3753</span> Loss Minimization by Distributed Generation Allocation in Radial Distribution System Using Crow Search Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Nageswara%20Rao">M. Nageswara Rao</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20S.%20N.%20K.%20Chaitanya"> V. S. N. K. Chaitanya</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Amarendranath"> K. Amarendranath</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an optimal allocation and sizing of Distributed Generation (DG) in Radial Distribution Network (RDN) for total power loss minimization and enhances the voltage profile of the system. The two main important part of this study first is to find optimal allocation and second is optimum size of DG. The locations of DGs are identified by Analytical expressions and crow search algorithm has been employed to determine the optimum size of DG. In this study, the DG has been placed on single and multiple allocations.CSA is a meta-heuristic algorithm inspired by the intelligent behavior of the crows. Crows stores their excess food in different locations and memorizes those locations to retrieve it when it is needed. They follow each other to do thievery to obtain better food source. This analysis is tested on IEEE 33 bus and IEEE 69 bus under MATLAB environment and the results are compared with existing methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=analytical%20expression" title="analytical expression">analytical expression</a>, <a href="https://publications.waset.org/abstracts/search?q=distributed%20generation" title=" distributed generation"> distributed generation</a>, <a href="https://publications.waset.org/abstracts/search?q=crow%20search%20algorithm" title=" crow search algorithm"> crow search algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20loss" title=" power loss"> power loss</a>, <a href="https://publications.waset.org/abstracts/search?q=voltage%20profile" title=" voltage profile"> voltage profile</a> </p> <a href="https://publications.waset.org/abstracts/104210/loss-minimization-by-distributed-generation-allocation-in-radial-distribution-system-using-crow-search-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/104210.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">235</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3752</span> Discrete Swarm with Passive Congregation for Cost Minimization of the Multiple Vehicle Routing Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tarek%20Aboueldahab">Tarek Aboueldahab</a>, <a href="https://publications.waset.org/abstracts/search?q=Hanan%20Farag"> Hanan Farag</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cost minimization of Multiple Vehicle Routing Problem becomes a critical issue in the field of transportation because it is NP-hard optimization problem and the search space is complex. Many researches use the hybridization of artificial intelligence (AI) models to solve this problem; however, it can not guarantee to reach the best solution due to the difficulty of searching the whole search space. To overcome this problem, we introduce the hybrid model of Discrete Particle Swarm Optimization (DPSO) with a passive congregation which enable searching the whole search space to compromise between both local and global search. The practical experiment shows that our model obviously outperforms other hybrid models in cost minimization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cost%20minimization" title="cost minimization">cost minimization</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-vehicle%20routing%20problem" title=" multi-vehicle routing problem"> multi-vehicle routing problem</a>, <a href="https://publications.waset.org/abstracts/search?q=passive%20congregation" title=" passive congregation"> passive congregation</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20swarm" title=" discrete swarm"> discrete swarm</a>, <a href="https://publications.waset.org/abstracts/search?q=passive%20congregation" title=" passive congregation"> passive congregation</a> </p> <a href="https://publications.waset.org/abstracts/157025/discrete-swarm-with-passive-congregation-for-cost-minimization-of-the-multiple-vehicle-routing-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157025.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">98</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3751</span> Minimization Entropic Applied to Rotary Dryers to Reduce the Energy Consumption</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=I.%20O.%20Nascimento">I. O. Nascimento</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20T.%20Manzi"> J. T. Manzi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The drying process is an important operation in the chemical industry and it is widely used in the food, grain industry and fertilizer industry. However, for demanding a considerable consumption of energy, such a process requires a deep energetic analysis in order to reduce operating costs. This paper deals with thermodynamic optimization applied to rotary dryers based on the entropy production minimization, aiming at to reduce the energy consumption. To do this, the mass, energy and entropy balance was used for developing a relationship that represents the rate of entropy production. The use of the Second Law of Thermodynamics is essential because it takes into account constraints of nature. Since the entropy production rate is minimized, optimals conditions of operations can be established and the process can obtain a substantial gain in energy saving. The minimization strategy had been led using classical methods such as Lagrange multipliers and implemented in the MATLAB platform. As expected, the preliminary results reveal a significant energy saving by the application of the optimal parameters found by the procedure of the entropy minimization It is important to say that this method has shown easy implementation and low cost. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=thermodynamic%20optimization" title="thermodynamic optimization">thermodynamic optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=drying" title=" drying"> drying</a>, <a href="https://publications.waset.org/abstracts/search?q=entropy%20minimization" title=" entropy minimization"> entropy minimization</a>, <a href="https://publications.waset.org/abstracts/search?q=modeling%20dryers" title=" modeling dryers"> modeling dryers</a> </p> <a href="https://publications.waset.org/abstracts/45815/minimization-entropic-applied-to-rotary-dryers-to-reduce-the-energy-consumption" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45815.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">258</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3750</span> Discrete Breeding Swarm for Cost Minimization of Parallel Job Shop Scheduling Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tarek%20Aboueldahab">Tarek Aboueldahab</a>, <a href="https://publications.waset.org/abstracts/search?q=Hanan%20Farag"> Hanan Farag</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Parallel Job Shop Scheduling Problem (JSP) is a multi-objective and multi constrains NP- optimization problem. Traditional Artificial Intelligence techniques have been widely used; however, they could be trapped into the local minimum without reaching the optimum solution, so we propose a hybrid Artificial Intelligence model (AI) with Discrete Breeding Swarm (DBS) added to traditional Artificial Intelligence to avoid this trapping. This model is applied in the cost minimization of the Car Sequencing and Operator Allocation (CSOA) problem. The practical experiment shows that our model outperforms other techniques in cost minimization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=parallel%20job%20shop%20scheduling%20problem" title="parallel job shop scheduling problem">parallel job shop scheduling problem</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20breeding%20swarm" title=" discrete breeding swarm"> discrete breeding swarm</a>, <a href="https://publications.waset.org/abstracts/search?q=car%20sequencing%20and%20operator%20allocation" title=" car sequencing and operator allocation"> car sequencing and operator allocation</a>, <a href="https://publications.waset.org/abstracts/search?q=cost%20minimization" title=" cost minimization"> cost minimization</a> </p> <a href="https://publications.waset.org/abstracts/132701/discrete-breeding-swarm-for-cost-minimization-of-parallel-job-shop-scheduling-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/132701.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">188</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3749</span> Synthesis of Balanced 3-RRR Planar Parallel Manipulators</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arakelian%20Vigen">Arakelian Vigen</a>, <a href="https://publications.waset.org/abstracts/search?q=Geng%20Jing"> Geng Jing</a>, <a href="https://publications.waset.org/abstracts/search?q=Le%20Baron%20Jean-Paul"> Le Baron Jean-Paul</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper deals with the design of parallel manipulators with balanced inertia forces and moments. The balancing of the resultant of the inertia forces of 3-RRR planar parallel manipulators is carried out through mass redistribution and centre of mass acceleration minimization. The proposed balancing technique is achieved in two steps: at first, optimal redistribution of the masses of input links is accomplished, which ensures the similarity of the end-effector trajectory and the manipulator’s common centre of mass trajectory, then, optimal trajectory planning of the end-effector by 'bang-bang' profile is reached. In such a way, the minimization of the magnitude of the acceleration of the centre of mass of the manipulator brings about a minimization of shaking force. To minimize the resultant of the inertia moments (shaking moment), the active balancing via inertia flywheel is applied. However, in this case, the active balancing is quite different from previous applications because it provides only a partial cancellation of the shaking moment due to the incomplete balancing of shaking force. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dynamic%20balancing" title="dynamic balancing">dynamic balancing</a>, <a href="https://publications.waset.org/abstracts/search?q=inertia%20force%20minimization" title=" inertia force minimization"> inertia force minimization</a>, <a href="https://publications.waset.org/abstracts/search?q=inertia%20moment%20minimization" title=" inertia moment minimization"> inertia moment minimization</a>, <a href="https://publications.waset.org/abstracts/search?q=3-RRR%20planar%20parallel%20manipulator" title=" 3-RRR planar parallel manipulator"> 3-RRR planar parallel manipulator</a> </p> <a href="https://publications.waset.org/abstracts/70145/synthesis-of-balanced-3-rrr-planar-parallel-manipulators" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/70145.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">463</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3748</span> Analysis of the Significance of Multimedia Channels Using Sparse PCA and Regularized SVD</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kourosh%20Modarresi">Kourosh Modarresi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The abundance of media channels and devices has given users a variety of options to extract, discover, and explore information in the digital world. Since, often, there is a long and complicated path that a typical user may venture before taking any (significant) action (such as purchasing goods and services), it is critical to know how each node (media channel) in the path of user has contributed to the final action. In this work, the significance of each media channel is computed using statistical analysis and machine learning techniques. More specifically, “Regularized Singular Value Decomposition”, and “Sparse Principal Component” has been used to compute the significance of each channel toward the final action. The results of this work are a considerable improvement compared to the present approaches. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multimedia%20attribution" title="multimedia attribution">multimedia attribution</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20principal%20component" title=" sparse principal component"> sparse principal component</a>, <a href="https://publications.waset.org/abstracts/search?q=regularization" title=" regularization"> regularization</a>, <a href="https://publications.waset.org/abstracts/search?q=singular%20value%20decomposition" title=" singular value decomposition"> singular value decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20significance" title=" feature significance"> feature significance</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20systems" title=" linear systems"> linear systems</a>, <a href="https://publications.waset.org/abstracts/search?q=variable%20shrinkage" title=" variable shrinkage"> variable shrinkage</a> </p> <a href="https://publications.waset.org/abstracts/19533/analysis-of-the-significance-of-multimedia-channels-using-sparse-pca-and-regularized-svd" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19533.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">309</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3747</span> Evaluation of Minimization of Moment Ratio Method by Physical Modeling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amin%20Eslami">Amin Eslami</a>, <a href="https://publications.waset.org/abstracts/search?q=Jafar%20Bolouri%20Bazaz"> Jafar Bolouri Bazaz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Under active stress conditions, a rigid cantilever retaining wall tends to rotate about a pivot point located within the embedded depth of the wall. For purely granular and cohesive soils, a methodology was previously reported called minimization of moment ratio to determine the location of the pivot point of rotation. The usage of this new methodology is to estimate the rotational stability safety factor. Moreover, the degree of improvement required in a backfill to get a desired safety factor can be estimated by the concept of the shear strength demand. In this article, the accuracy of this method for another type of cantilever walls called Contiguous Bored Pile (CBP) retaining wall is evaluated by using physical modeling technique. Based on observations, the results of moment ratio minimization method are in good agreement with the results of the carried out physical modeling. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cantilever%20retaining%20wall" title="cantilever retaining wall">cantilever retaining wall</a>, <a href="https://publications.waset.org/abstracts/search?q=physical%20modeling" title=" physical modeling"> physical modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=minimization%20of%20moment%20ratio%20method" title=" minimization of moment ratio method"> minimization of moment ratio method</a>, <a href="https://publications.waset.org/abstracts/search?q=pivot%20point" title=" pivot point "> pivot point </a> </p> <a href="https://publications.waset.org/abstracts/26383/evaluation-of-minimization-of-moment-ratio-method-by-physical-modeling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26383.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">332</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3746</span> Minimization of Switching Losses in Cascaded Multilevel Inverters Using Efficient Sequential Switching Hybrid-Modulation Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20Satish%20Kumar">P. Satish Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Ramakrishna"> K. Ramakrishna</a>, <a href="https://publications.waset.org/abstracts/search?q=Ch.%20Lokeshwar%20Reddy"> Ch. Lokeshwar Reddy</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20Sridhar"> G. Sridhar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents two different sequential switching hybrid-modulation strategies and implemented for cascaded multilevel inverters. Hybrid modulation strategies represent the combinations of Fundamental-Frequency Pulse Width Modulation (FFPWM) and Multilevel Sinusoidal-Modulation (MSPWM) strategies, and are designed for performance of the well-known Alternative Phase Opposition Disposition (APOD), Phase Shifted Carrier (PSC). The main characteristics of these modulations are the reduction of switching losses with good harmonic performance, balanced power loss dissipation among the devices with in a cell, and among the series-connected cells. The feasibility of these modulations is verified through spectral analysis, power loss analysis and simulation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cascaded%20multilevel%20inverters" title="cascaded multilevel inverters">cascaded multilevel inverters</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20modulation" title=" hybrid modulation"> hybrid modulation</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20loss%20analysis" title=" power loss analysis"> power loss analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=pulse%20width%20modulation" title=" pulse width modulation"> pulse width modulation</a> </p> <a href="https://publications.waset.org/abstracts/7094/minimization-of-switching-losses-in-cascaded-multilevel-inverters-using-efficient-sequential-switching-hybrid-modulation-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7094.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">537</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3745</span> Sum Capacity with Regularized Channel Inversion in Multi-Antenna Downlink Systems under Equal Power Constraint</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Attaullah%20Khawaja">Attaullah Khawaja</a>, <a href="https://publications.waset.org/abstracts/search?q=Amna%20Shabbir"> Amna Shabbir </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Channel inversion is one of the simplest techniques for multiuser downlink systems with single-antenna users. In this paper regularized channel inversion under equal power constraint in the multiuser multiple input multiple output (MU-MIMO) broadcast channels has been considered. Sum capacity with plain channel inversion also known as Zero Forcing Beam Forming (ZFBF) and optimum sum capacity using Dirty Paper Coding (DPC) has also been investigated. Analysis and simulations show that regularization enhances the system performance and empower linear growth in Sum Capacity and specially work well at low signal to noise ratio (SNRs) regime. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=broadcast%20channel" title="broadcast channel">broadcast channel</a>, <a href="https://publications.waset.org/abstracts/search?q=channel%20inversion" title=" channel inversion"> channel inversion</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20antenna%20multiple-user%20wireless" title=" multiple antenna multiple-user wireless"> multiple antenna multiple-user wireless</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple-input%20multiple-output%20%28MIMO%29" title=" multiple-input multiple-output (MIMO)"> multiple-input multiple-output (MIMO)</a>, <a href="https://publications.waset.org/abstracts/search?q=regularization" title=" regularization"> regularization</a>, <a href="https://publications.waset.org/abstracts/search?q=dirty%20paper%20coding%20%28DPC%29" title=" dirty paper coding (DPC)"> dirty paper coding (DPC)</a>, <a href="https://publications.waset.org/abstracts/search?q=sum%20capacity" title=" sum capacity"> sum capacity</a> </p> <a href="https://publications.waset.org/abstracts/16732/sum-capacity-with-regularized-channel-inversion-in-multi-antenna-downlink-systems-under-equal-power-constraint" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16732.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">527</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3744</span> A Method of Effective Planning and Control of Industrial Facility Energy Consumption</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aleksandra%20Aleksandrovna%20Filimonova">Aleksandra Aleksandrovna Filimonova</a>, <a href="https://publications.waset.org/abstracts/search?q=Lev%20Sergeevich%20Kazarinov"> Lev Sergeevich Kazarinov</a>, <a href="https://publications.waset.org/abstracts/search?q=Tatyana%20Aleksandrovna%20Barbasova"> Tatyana Aleksandrovna Barbasova</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A method of effective planning and control of industrial facility energy consumption is offered. The method allows to optimally arrange the management and full control of complex production facilities in accordance with the criteria of minimal technical and economic losses at the forecasting control. The method is based on the optimal construction of the power efficiency characteristics with the prescribed accuracy. The problem of optimal designing of the forecasting model is solved on the basis of three criteria: maximizing the weighted sum of the points of forecasting with the prescribed accuracy; the solving of the problem by the standard principles at the incomplete statistic data on the basis of minimization of the regularized function; minimizing the technical and economic losses due to the forecasting errors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=energy%20consumption" title="energy consumption">energy consumption</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20efficiency" title=" energy efficiency"> energy efficiency</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20management%20system" title=" energy management system"> energy management system</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting%20model" title=" forecasting model"> forecasting model</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20efficiency%20characteristics" title=" power efficiency characteristics"> power efficiency characteristics</a> </p> <a href="https://publications.waset.org/abstracts/38726/a-method-of-effective-planning-and-control-of-industrial-facility-energy-consumption" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/38726.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">393</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3743</span> An Improved Approach to Solve Two-Level Hierarchical Time Minimization Transportation Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kalpana%20Dahiya">Kalpana Dahiya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper discusses a two-level hierarchical time minimization transportation problem, which is an important class of transportation problems arising in industries. This problem has been studied by various researchers, and a number of polynomial time iterative algorithms are available to find its solution. All the existing algorithms, though efficient, have some shortcomings. The current study proposes an alternate solution algorithm for the problem that is more efficient in terms of computational time than the existing algorithms. The results justifying the underlying theory of the proposed algorithm are given. Further, a detailed comparison of the computational behaviour of all the algorithms for randomly generated instances of this problem of different sizes validates the efficiency of the proposed algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=global%20optimization" title="global optimization">global optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20optimization" title=" hierarchical optimization"> hierarchical optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=transportation%20problem" title=" transportation problem"> transportation problem</a>, <a href="https://publications.waset.org/abstracts/search?q=concave%20minimization" title=" concave minimization"> concave minimization</a> </p> <a href="https://publications.waset.org/abstracts/122713/an-improved-approach-to-solve-two-level-hierarchical-time-minimization-transportation-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/122713.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">162</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3742</span> Numerical Computation of Generalized Rosenau Regularized Long-Wave Equation via B-Spline Over Butcher’s Fifth Order Runge-Kutta Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Guesh%20Simretab%20Gebremedhin">Guesh Simretab Gebremedhin</a>, <a href="https://publications.waset.org/abstracts/search?q=Saumya%20Rajan%20Jena"> Saumya Rajan Jena</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, a septic B-spline scheme has been used to simplify the process of solving an approximate solution of the generalized Rosenau-regularized long-wave equation (GR-RLWE) with initial boundary conditions. The resulting system of first-order ODEs has dealt with Butcher’s fifth order Runge-Kutta (BFRK) approach without using finite difference techniques for discretizing the time-dependent variables at each time level. Here, no transformation or any kind of linearization technique is employed to tackle the nonlinearity of the equation. Two test problems have been selected for numerical justifications and comparisons with other researchers on the basis of efficiency, accuracy, and results of the two invariants Mᵢ (mass) and Eᵢ (energy) of some motion that has been used to test the conservative properties of the proposed scheme. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=septic%20B-spline%20scheme" title="septic B-spline scheme">septic B-spline scheme</a>, <a href="https://publications.waset.org/abstracts/search?q=Butcher%27s%20fifth%20order%20Runge-Kutta%20approach" title=" Butcher's fifth order Runge-Kutta approach"> Butcher's fifth order Runge-Kutta approach</a>, <a href="https://publications.waset.org/abstracts/search?q=error%20norms" title=" error norms"> error norms</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20Rosenau-RLW%20equation" title=" generalized Rosenau-RLW equation"> generalized Rosenau-RLW equation</a> </p> <a href="https://publications.waset.org/abstracts/181195/numerical-computation-of-generalized-rosenau-regularized-long-wave-equation-via-b-spline-over-butchers-fifth-order-runge-kutta-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/181195.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">66</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3741</span> Yield Loss Estimation Using Multiple Drought Severity Indices</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sara%20Tokhi%20Arab">Sara Tokhi Arab</a>, <a href="https://publications.waset.org/abstracts/search?q=Rozo%20Noguchi"> Rozo Noguchi</a>, <a href="https://publications.waset.org/abstracts/search?q=Tofeal%20Ahamed"> Tofeal Ahamed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Drought is a natural disaster that occurs in a region due to a lack of precipitation and high temperatures over a continuous period or in a single season as a consequence of climate change. Precipitation deficits and prolonged high temperatures mostly affect the agricultural sector, water resources, socioeconomics, and the environment. Consequently, it causes agricultural product loss, food shortage, famines, migration, and natural resources degradation in a region. Agriculture is the first sector affected by drought. Therefore, it is important to develop an agricultural drought risk and loss assessment to mitigate the drought impact in the agriculture sector. In this context, the main purpose of this study was to assess yield loss using composite drought indices in the drought-affected vineyards. In this study, the CDI was developed for the years 2016 to 2020 by comprising five indices: the vegetation condition index (VCI), temperature condition index (TCI), deviation of NDVI from the long-term mean (NDVI DEV), normalized difference moisture index (NDMI) and precipitation condition index (PCI). Moreover, the quantitative principal component analysis (PCA) approach was used to assign a weight for each input parameter, and then the weights of all the indices were combined into one composite drought index. Finally, Bayesian regularized artificial neural networks (BRANNs) were used to evaluate the yield variation in each affected vineyard. The composite drought index result indicated the moderate to severe droughts were observed across the Kabul Province during 2016 and 2018. Moreover, the results showed that there was no vineyard in extreme drought conditions. Therefore, we only considered the severe and moderated condition. According to the BRANNs results R=0.87 and R=0.94 in severe drought conditions for the years of 2016 and 2018 and the R= 0.85 and R=0.91 in moderate drought conditions for the years of 2016 and 2018, respectively. In the Kabul Province within the two years drought periods, there was a significate deficit in the vineyards. According to the findings, 2018 had the highest rate of loss almost -7 ton/ha. However, in 2016 the loss rates were about – 1.2 ton/ha. This research will support stakeholders to identify drought affect vineyards and support farmers during severe drought. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=grapes" title="grapes">grapes</a>, <a href="https://publications.waset.org/abstracts/search?q=composite%20drought%20index" title=" composite drought index"> composite drought index</a>, <a href="https://publications.waset.org/abstracts/search?q=yield%20loss" title=" yield loss"> yield loss</a>, <a href="https://publications.waset.org/abstracts/search?q=satellite%20remote%20sensing" title=" satellite remote sensing"> satellite remote sensing</a> </p> <a href="https://publications.waset.org/abstracts/143211/yield-loss-estimation-using-multiple-drought-severity-indices" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143211.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">157</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3740</span> Effect of DG Installation in Distribution System for Voltage Monitoring Scheme</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20R.%20A.%20Rahim">S. R. A. Rahim</a>, <a href="https://publications.waset.org/abstracts/search?q=I.%20Musirin"> I. Musirin</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20M.%20Othman"> M. M. Othman</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20H.%20Hussain"> M. H. Hussain</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Loss minimization is a long progressing issue mainly in distribution system. Nevertheless, its effect led to temperature rise due to significant voltage drop through the distribution line. Thus, compensation scheme should be proper scheduled in the attempt to alleviate the voltage drop phenomenon. Distributed generation has been profoundly known for voltage profile improvement provided that over-compensation or under-compensation phenomena are avoided. This paper addresses the issue of voltage improvement through different type DG installation. In ensuring optimal sizing and location of the DGs, predeveloped EMEFA technique was made to be used for this purpose. Incremental loading condition subjected to the system is the concern such that it is beneficial to the power system operator. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distributed%20generation" title="distributed generation">distributed generation</a>, <a href="https://publications.waset.org/abstracts/search?q=EMEFA" title=" EMEFA"> EMEFA</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20loss" title=" power loss"> power loss</a>, <a href="https://publications.waset.org/abstracts/search?q=voltage%20profile" title=" voltage profile"> voltage profile</a> </p> <a href="https://publications.waset.org/abstracts/3286/effect-of-dg-installation-in-distribution-system-for-voltage-monitoring-scheme" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3286.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">367</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3739</span> Optimization of Line Loss Minimization Using Distributed Generation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Sambath">S. Sambath</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20Palanivel"> P. Palanivel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Research conducted in the last few decades has proven that an inclusion of Distributed Genaration (DG) into distribution systems considerably lowers the level of power losses and the power quality improved. Moreover, the choice of DG is even more attractive since it provides not only benefits in power loss minimisation, but also a wide range of other advantages including environment, economic, power qualities and technical issues. This paper is an intent to quantify and analyse the impact of distributed generation (DG) in Tamil Nadu, India to examine what the benefits of decentralized generation would be for meeting rural loads. We used load flow analysis to simulate and quantify the loss reduction and power quality enhancement by having decentralized generation available line conditions for actual rural feeders in Tamil Nadu, India. Reactive and voltage profile was considered. This helps utilities to better plan their system in rural areas to meet dispersed loads, while optimizing the renewable and decentralised generation sources. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distributed%20generation" title="distributed generation">distributed generation</a>, <a href="https://publications.waset.org/abstracts/search?q=distribution%20system" title=" distribution system"> distribution system</a>, <a href="https://publications.waset.org/abstracts/search?q=load%20flow%20analysis" title=" load flow analysis"> load flow analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20location" title=" optimal location"> optimal location</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20quality" title=" power quality"> power quality</a> </p> <a href="https://publications.waset.org/abstracts/4401/optimization-of-line-loss-minimization-using-distributed-generation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4401.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">400</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3738</span> Online Robust Model Predictive Control for Linear Fractional Transformation Systems Using Linear Matrix Inequalities</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Peyman%20Sindareh%20Esfahani">Peyman Sindareh Esfahani</a>, <a href="https://publications.waset.org/abstracts/search?q=Jeffery%20Kurt%20Pieper"> Jeffery Kurt Pieper</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the problem of robust model predictive control (MPC) for discrete-time linear systems in linear fractional transformation form with structured uncertainty and norm-bounded disturbance is investigated. The problem of minimization of the cost function for MPC design is converted to minimization of the worst case of the cost function. Then, this problem is reduced to minimization of an upper bound of the cost function subject to a terminal inequality satisfying the <em>l</em><sub>2</sub>-norm of the closed loop system. The characteristic of the linear fractional transformation system is taken into account, and by using some mathematical tools, the robust predictive controller design problem is turned into a linear matrix inequality minimization problem. Afterwards, a formulation which includes an integrator to improve the performance of the proposed robust model predictive controller in steady state condition is studied. The validity of the approaches is illustrated through a robust control benchmark problem. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=linear%20fractional%20transformation" title="linear fractional transformation">linear fractional transformation</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20matrix%20inequality" title=" linear matrix inequality"> linear matrix inequality</a>, <a href="https://publications.waset.org/abstracts/search?q=robust%20model%20predictive%20control" title=" robust model predictive control"> robust model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=state%20feedback%20control" title=" state feedback control"> state feedback control</a> </p> <a href="https://publications.waset.org/abstracts/69466/online-robust-model-predictive-control-for-linear-fractional-transformation-systems-using-linear-matrix-inequalities" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/69466.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">395</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3737</span> Proximal Method of Solving Split System of Minimization Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anteneh%20Getachew%20Gebrie">Anteneh Getachew Gebrie</a>, <a href="https://publications.waset.org/abstracts/search?q=Rabian%20Wangkeeree"> Rabian Wangkeeree</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this paper is to introduce iterative algorithm solving split system of minimization problem given as a task of ﬁnding a common minimizer point of ﬁnite family of proper, lower semicontinuous convex functions and whose image under a bounded linear operator is also common minimizer point of another ﬁnite family of proper, lower semicontinuous convex functions. We obtain strong convergence of the sequence generated by our algorithm under some suitable conditions on the parameters. The iterative schemes are developed with a way of selecting the step sizes such that the information of operator norm is not necessary. Some applications and numerical experiment is given to analyse the eﬃciency of our algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hilbert%20Space" title="Hilbert Space">Hilbert Space</a>, <a href="https://publications.waset.org/abstracts/search?q=minimization%20problems" title=" minimization problems"> minimization problems</a>, <a href="https://publications.waset.org/abstracts/search?q=Moreau-Yosida%20approximate" title=" Moreau-Yosida approximate"> Moreau-Yosida approximate</a>, <a href="https://publications.waset.org/abstracts/search?q=split%20feasibility%20problem" title=" split feasibility problem"> split feasibility problem</a> </p> <a href="https://publications.waset.org/abstracts/119147/proximal-method-of-solving-split-system-of-minimization-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/119147.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">144</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3736</span> Clustering Based Level Set Evaluation for Low Contrast Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bikshalu%20Kalagadda">Bikshalu Kalagadda</a>, <a href="https://publications.waset.org/abstracts/search?q=Srikanth%20Rangu"> Srikanth Rangu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The important object of images segmentation is to extract objects with respect to some input features. One of the important methods for image segmentation is Level set method. Generally medical images and synthetic images with low contrast of pixel profile, for such images difficult to locate interested features in images. In conventional level set function, develops irregularity during its process of evaluation of contour of objects, this destroy the stability of evolution process. For this problem a remedy is proposed, a new hybrid algorithm is Clustering Level Set Evolution. Kernel fuzzy particles swarm optimization clustering with the Distance Regularized Level Set (DRLS) and Selective Binary, and Gaussian Filtering Regularized Level Set (SBGFRLS) methods are used. The ability of identifying different regions becomes easy with improved speed. Efficiency of the modified method can be evaluated by comparing with the previous method for similar specifications. Comparison can be carried out by considering medical and synthetic images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=segmentation" title="segmentation">segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=level%20set%20function" title=" level set function"> level set function</a>, <a href="https://publications.waset.org/abstracts/search?q=re-initialization" title=" re-initialization"> re-initialization</a>, <a href="https://publications.waset.org/abstracts/search?q=Kernel%20fuzzy" title=" Kernel fuzzy"> Kernel fuzzy</a>, <a href="https://publications.waset.org/abstracts/search?q=swarm%20optimization" title=" swarm optimization"> swarm optimization</a> </p> <a href="https://publications.waset.org/abstracts/65723/clustering-based-level-set-evaluation-for-low-contrast-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65723.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">352</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3735</span> Switched System Diagnosis Based on Intelligent State Filtering with Unknown Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nada%20Slimane">Nada Slimane</a>, <a href="https://publications.waset.org/abstracts/search?q=Foued%20Theljani"> Foued Theljani</a>, <a href="https://publications.waset.org/abstracts/search?q=Faouzi%20Bouani"> Faouzi Bouani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper addresses the problem of fault diagnosis for systems operating in several modes (normal or faulty) based on states assessment. We use, for this purpose, a methodology consisting of three main processes: 1) sequential data clustering, 2) linear model regression and 3) state filtering. Typically, Kalman Filter (KF) is an algorithm that provides estimation of unknown states using a sequence of I/O measurements. Inevitably, although it is an efficient technique for state estimation, it presents two main weaknesses. First, it merely predicts states without being able to isolate/classify them according to their different operating modes, whether normal or faulty modes. To deal with this dilemma, the KF is endowed with an extra clustering step based fully on sequential version of the k-means algorithm. Second, to provide state estimation, KF requires state space models, which can be unknown. A linear regularized regression is used to identify the required models. To prove its effectiveness, the proposed approach is assessed on a simulated benchmark. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clustering" title="clustering">clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=diagnosis" title=" diagnosis"> diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalman%20Filtering" title=" Kalman Filtering"> Kalman Filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=k-means" title=" k-means"> k-means</a>, <a href="https://publications.waset.org/abstracts/search?q=regularized%20regression" title=" regularized regression"> regularized regression</a> </p> <a href="https://publications.waset.org/abstracts/104370/switched-system-diagnosis-based-on-intelligent-state-filtering-with-unknown-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/104370.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">182</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3734</span> Linear Stability Analysis of a Regularized Two-Fluid Model for Unstable Gas-Liquid Flows in Long Hilly Terrain Pipelines</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=David%20Alejandro%20Lazo-Vasquez">David Alejandro Lazo-Vasquez</a>, <a href="https://publications.waset.org/abstracts/search?q=Jorge%20Luis%20Balino"> Jorge Luis Balino</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the petroleum industry, multiphase flow occurs when oil, gas, and water are transported in the same pipe through large pipeline systems. The flow can take different patterns depending on parameters like fluid velocities, pipe diameter, pipe inclination, and fluid properties. Mainly, intermittent flow is produced by the natural propagation of short and long waves, according to the Kelvin-Helmholtz Stability Theory. To model stratified flow and the onset of intermittent flow, it is crucial to have knowledge of short and long waves behavior. The two-fluid model, frequently employed for characterizing multiphase systems, becomes ill-posed for high liquid and gas velocities and large inclination angles, for short waves can develop infinite growth rates. We are interested in focusing attention on long-wave instability, which leads to the production of roll waves that may grow and result in the transition from stratified flow to intermittent flow. In this study, global and local linear stability analyses for dynamic and kinematic stability criteria predict the regions of stability of the flow for different pipe inclinations and fluid velocities in regularized and non-regularized systems, concurrently. It was possible to distinguish when: wave growth rates are absolutely bounded (stable stratified smooth flow), waves have finite growth rates (unstable stratified wavy flow), and when the equation system becomes elliptic and hyperbolization is needed. In order to bound short wave growth rates and regularize the equation system, we incorporated some lower and higher-order terms like interfacial drag and surface tension, respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=linear%20stability%20analysis" title="linear stability analysis">linear stability analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=multiphase%20flow" title=" multiphase flow"> multiphase flow</a>, <a href="https://publications.waset.org/abstracts/search?q=onset%20of%20slugging" title=" onset of slugging"> onset of slugging</a>, <a href="https://publications.waset.org/abstracts/search?q=two-fluid%20model%20regularization" title=" two-fluid model regularization"> two-fluid model regularization</a> </p> <a href="https://publications.waset.org/abstracts/113094/linear-stability-analysis-of-a-regularized-two-fluid-model-for-unstable-gas-liquid-flows-in-long-hilly-terrain-pipelines" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/113094.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">135</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3733</span> A Priority Based Imbalanced Time Minimization Assignment Problem: An Iterative Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ekta%20Jain">Ekta Jain</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalpana%20Dahiya"> Kalpana Dahiya</a>, <a href="https://publications.waset.org/abstracts/search?q=Vanita%20Verma"> Vanita Verma</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper discusses a priority based imbalanced time minimization assignment problem dealing with the allocation of n jobs to m < n persons in which the project is carried out in two stages, viz. Stage-I and Stage-II. Stage-I consists of n1 ( < m) primary jobs and Stage-II consists of remaining (n-n1) secondary jobs which are commenced only after primary jobs are finished. Each job is to be allocated to exactly one person, and each person has to do at least one job. It is assumed that nature of the Stage-I jobs is such that one person can do exactly one primary job whereas a person can do more than one secondary job in Stage-II. In a particular stage, all persons start doing the jobs simultaneously, but if a person is doing more than one job, he does them one after the other in any order. The aim of the proposed study is to find the feasible assignment which minimizes the total time for the two stage execution of the project. For this, an iterative algorithm is proposed, which at each iteration, solves a constrained imbalanced time minimization assignment problem to generate a pair of Stage-I and Stage-II times. For solving this constrained problem, an algorithm is developed in the current paper. Later, alternate combinations based method to solve the priority based imbalanced problem is also discussed and a comparative study is carried out. Numerical illustrations are provided in support of the theory. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=assignment" title="assignment">assignment</a>, <a href="https://publications.waset.org/abstracts/search?q=imbalanced" title=" imbalanced"> imbalanced</a>, <a href="https://publications.waset.org/abstracts/search?q=priority" title=" priority"> priority</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20minimization" title=" time minimization"> time minimization</a> </p> <a href="https://publications.waset.org/abstracts/75198/a-priority-based-imbalanced-time-minimization-assignment-problem-an-iterative-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75198.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">234</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3732</span> On the System of Split Equilibrium and Fixed Point Problems in Real Hilbert Spaces</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Francis%20O.%20Nwawuru">Francis O. Nwawuru</a>, <a href="https://publications.waset.org/abstracts/search?q=Jeremiah%20N.%20Ezeora"> Jeremiah N. Ezeora</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a new algorithm for solving the system of split equilibrium and fixed point problems in real Hilbert spaces is considered. The equilibrium bifunction involves a nite family of pseudo-monotone mappings, which is an improvement over monotone operators. More so, it turns out that the solution of the finite family of nonexpansive mappings. The regularized parameters do not depend on Lipschitz constants. Also, the computations of the stepsize, which plays a crucial role in the convergence analysis of the proposed method, do require prior knowledge of the norm of the involved bounded linear map. Furthermore, to speed up the rate of convergence, an inertial term technique is introduced in the proposed method. Under standard assumptions on the operators and the control sequences, using a modified Halpern iteration method, we establish strong convergence, a desired result in applications. Finally, the proposed scheme is applied to solve some optimization problems. The result obtained improves numerous results announced earlier in this direction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=equilibrium" title="equilibrium">equilibrium</a>, <a href="https://publications.waset.org/abstracts/search?q=Hilbert%20spaces" title=" Hilbert spaces"> Hilbert spaces</a>, <a href="https://publications.waset.org/abstracts/search?q=fixed%20point" title=" fixed point"> fixed point</a>, <a href="https://publications.waset.org/abstracts/search?q=nonexpansive%20mapping" title=" nonexpansive mapping"> nonexpansive mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=extragradient%20method" title=" extragradient method"> extragradient method</a>, <a href="https://publications.waset.org/abstracts/search?q=regularized%20equilibrium" title=" regularized equilibrium"> regularized equilibrium</a> </p> <a href="https://publications.waset.org/abstracts/184412/on-the-system-of-split-equilibrium-and-fixed-point-problems-in-real-hilbert-spaces" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/184412.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">48</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3731</span> Loss Allocation in Radial Distribution Networks for Loads of Composite Types</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sumit%20Banerjee">Sumit Banerjee</a>, <a href="https://publications.waset.org/abstracts/search?q=Chandan%20Kumar%20Chanda"> Chandan Kumar Chanda</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper presents allocation of active power losses and energy losses to consumers connected to radial distribution networks in a deregulated environment for loads of composite types. A detailed comparison among four algorithms, namely quadratic loss allocation, proportional loss allocation, pro rata loss allocation and exact loss allocation methods are presented. Quadratic and proportional loss allocations are based on identifying the active and reactive components of current in each branch and the losses are allocated to each consumer, pro rata loss allocation method is based on the load demand of each consumer and exact loss allocation method is based on the actual contribution of active power loss by each consumer. The effectiveness of the proposed comparison among four algorithms for composite load is demonstrated through an example. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=composite%20type" title="composite type">composite type</a>, <a href="https://publications.waset.org/abstracts/search?q=deregulation" title=" deregulation"> deregulation</a>, <a href="https://publications.waset.org/abstracts/search?q=loss%20allocation" title=" loss allocation"> loss allocation</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20distribution%20networks" title=" radial distribution networks"> radial distribution networks</a> </p> <a href="https://publications.waset.org/abstracts/42700/loss-allocation-in-radial-distribution-networks-for-loads-of-composite-types" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42700.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">286</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3730</span> 3D Liver Segmentation from CT Images Using a Level Set Method Based on a Shape and Intensity Distribution Prior</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nuseiba%20M.%20Altarawneh">Nuseiba M. Altarawneh</a>, <a href="https://publications.waset.org/abstracts/search?q=Suhuai%20Luo"> Suhuai Luo</a>, <a href="https://publications.waset.org/abstracts/search?q=Brian%20Regan"> Brian Regan</a>, <a href="https://publications.waset.org/abstracts/search?q=Guijin%20Tang"> Guijin Tang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Liver segmentation from medical images poses more challenges than analogous segmentations of other organs. This contribution introduces a liver segmentation method from a series of computer tomography images. Overall, we present a novel method for segmenting liver by coupling density matching with shape priors. Density matching signifies a tracking method which operates via maximizing the Bhattacharyya similarity measure between the photometric distribution from an estimated image region and a model photometric distribution. Density matching controls the direction of the evolution process and slows down the evolving contour in regions with weak edges. The shape prior improves the robustness of density matching and discourages the evolving contour from exceeding liver’s boundaries at regions with weak boundaries. The model is implemented using a modified distance regularized level set (DRLS) model. The experimental results show that the method achieves a satisfactory result. By comparing with the original DRLS model, it is evident that the proposed model herein is more effective in addressing the over segmentation problem. Finally, we gauge our performance of our model against matrices comprising of accuracy, sensitivity and specificity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bhattacharyya%20distance" title="Bhattacharyya distance">Bhattacharyya distance</a>, <a href="https://publications.waset.org/abstracts/search?q=distance%20regularized%20level%20set%20%28DRLS%29%20model" title=" distance regularized level set (DRLS) model"> distance regularized level set (DRLS) model</a>, <a href="https://publications.waset.org/abstracts/search?q=liver%20segmentation" title=" liver segmentation"> liver segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=level%20set%20method" title=" level set method"> level set method</a> </p> <a href="https://publications.waset.org/abstracts/35588/3d-liver-segmentation-from-ct-images-using-a-level-set-method-based-on-a-shape-and-intensity-distribution-prior" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35588.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">313</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3729</span> Pareto System of Optimal Placement and Sizing of Distributed Generation in Radial Distribution Networks Using Particle Swarm Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sani%20M.%20Lawal">Sani M. Lawal</a>, <a href="https://publications.waset.org/abstracts/search?q=Idris%20Musa"> Idris Musa</a>, <a href="https://publications.waset.org/abstracts/search?q=Aliyu%20D.%20Usman"> Aliyu D. Usman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Pareto approach of optimal solutions in a search space that evolved in multi-objective optimization problems is adopted in this paper, which stands for a set of solutions in the search space. This paper aims at presenting an optimal placement of Distributed Generation (DG) in radial distribution networks with an optimal size for minimization of power loss and voltage deviation as well as maximizing voltage profile of the networks. And these problems are formulated using particle swarm optimization (PSO) as a constraint nonlinear optimization problem with both locations and sizes of DG being continuous. The objective functions adopted are the total active power loss function and voltage deviation function. The multiple nature of the problem, made it necessary to form a multi-objective function in search of the solution that consists of both the DG location and size. The proposed PSO algorithm is used to determine optimal placement and size of DG in a distribution network. The output indicates that PSO algorithm technique shows an edge over other types of search methods due to its effectiveness and computational efficiency. The proposed method is tested on the standard IEEE 34-bus and validated with 33-bus test systems distribution networks. Results indicate that the sizing and location of DG are system dependent and should be optimally selected before installing the distributed generators in the system and also an improvement in the voltage profile and power loss reduction have been achieved. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distributed%20generation" title="distributed generation">distributed generation</a>, <a href="https://publications.waset.org/abstracts/search?q=pareto" title=" pareto"> pareto</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20loss" title=" power loss"> power loss</a>, <a href="https://publications.waset.org/abstracts/search?q=voltage%20deviation" title=" voltage deviation"> voltage deviation</a> </p> <a href="https://publications.waset.org/abstracts/54635/pareto-system-of-optimal-placement-and-sizing-of-distributed-generation-in-radial-distribution-networks-using-particle-swarm-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54635.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">364</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3728</span> A Holistic Approach for Technical Product Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Harald%20Lang">Harald Lang</a>, <a href="https://publications.waset.org/abstracts/search?q=Michael%20Bader"> Michael Bader</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Buchroithner"> A. Buchroithner</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Holistic methods covering the development process as a whole – e.g. systems engineering – have established themselves in product design. However, technical product optimization, representing improvements in efficiency and/or minimization of loss, usually applies to single components of a system. A holistic approach is being defined based on a hierarchical point of view of systems engineering. This is subsequently presented using the example of an electromechanical flywheel energy storage system for automotive applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=design" title="design">design</a>, <a href="https://publications.waset.org/abstracts/search?q=product%20development" title=" product development"> product development</a>, <a href="https://publications.waset.org/abstracts/search?q=product%20optimization" title=" product optimization"> product optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=systems%20engineering" title=" systems engineering"> systems engineering</a> </p> <a href="https://publications.waset.org/abstracts/35380/a-holistic-approach-for-technical-product-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35380.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">625</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3727</span> MapReduce Logistic Regression Algorithms with RHadoop</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Byung%20Ho%20Jung">Byung Ho Jung</a>, <a href="https://publications.waset.org/abstracts/search?q=Dong%20Hoon%20Lim"> Dong Hoon Lim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Logistic regression is a statistical method for analyzing a dataset in which there are one or more independent variables that determine an outcome. Logistic regression is used extensively in numerous disciplines, including the medical and social science fields. In this paper, we address the problem of estimating parameters in the logistic regression based on MapReduce framework with RHadoop that integrates R and Hadoop environment applicable to large scale data. There exist three learning algorithms for logistic regression, namely Gradient descent method, Cost minimization method and Newton-Rhapson's method. The Newton-Rhapson's method does not require a learning rate, while gradient descent and cost minimization methods need to manually pick a learning rate. The experimental results demonstrated that our learning algorithms using RHadoop can scale well and efficiently process large data sets on commodity hardware. We also compared the performance of our Newton-Rhapson's method with gradient descent and cost minimization methods. The results showed that our newton's method appeared to be the most robust to all data tested. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=logistic%20regression" title=" logistic regression"> logistic regression</a>, <a href="https://publications.waset.org/abstracts/search?q=MapReduce" title=" MapReduce"> MapReduce</a>, <a href="https://publications.waset.org/abstracts/search?q=RHadoop" title=" RHadoop"> RHadoop</a> </p> <a href="https://publications.waset.org/abstracts/41569/mapreduce-logistic-regression-algorithms-with-rhadoop" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41569.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">285</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3726</span> Bayesian Estimation under Different Loss Functions Using Gamma Prior for the Case of Exponential Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Md.%20Rashidul%20Hasan">Md. Rashidul Hasan</a>, <a href="https://publications.waset.org/abstracts/search?q=Atikur%20Rahman%20Baizid"> Atikur Rahman Baizid</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Bayesian estimation approach is a non-classical estimation technique in statistical inference and is very useful in real world situation. The aim of this paper is to study the Bayes estimators of the parameter of exponential distribution under different loss functions and then compared among them as well as with the classical estimator named maximum likelihood estimator (MLE). In our real life, we always try to minimize the loss and we also want to gather some prior information (distribution) about the problem to solve it accurately. Here the gamma prior is used as the prior distribution of exponential distribution for finding the Bayes estimator. In our study, we also used different symmetric and asymmetric loss functions such as squared error loss function, quadratic loss function, modified linear exponential (MLINEX) loss function and non-linear exponential (NLINEX) loss function. Finally, mean square error (MSE) of the estimators are obtained and then presented graphically. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayes%20estimator" title="Bayes estimator">Bayes estimator</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20likelihood%20estimator%20%28MLE%29" title=" maximum likelihood estimator (MLE)"> maximum likelihood estimator (MLE)</a>, <a href="https://publications.waset.org/abstracts/search?q=modified%20linear%20exponential%20%28MLINEX%29%20loss%20function" title=" modified linear exponential (MLINEX) loss function"> modified linear exponential (MLINEX) loss function</a>, <a href="https://publications.waset.org/abstracts/search?q=Squared%20Error%20%28SE%29%20loss%20function" title=" Squared Error (SE) loss function"> Squared Error (SE) loss function</a>, <a href="https://publications.waset.org/abstracts/search?q=non-linear%20exponential%20%28NLINEX%29%20loss%20function" title=" non-linear exponential (NLINEX) loss function"> non-linear exponential (NLINEX) loss function</a> </p> <a href="https://publications.waset.org/abstracts/53902/bayesian-estimation-under-different-loss-functions-using-gamma-prior-for-the-case-of-exponential-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/53902.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">384</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" 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